Big Multidimensional Datasets Visualization Using Neural Networks – Efficient Decision Support

Gintautas Dzemyda, Viktor Medvedev, Audrone Lupeikiene, Olga Kurasova, Albertas Caplinskas


Nowadays business information systems are thought of as decision-oriented systems supported by different types of subsystems. Multidimensional data visualization is an essential part of such systems. As datasets tend to be increasingly large, more effective ways are required to display, analyze and interpret information they contain. Most of the classical visualization methods are unsuitable for large datasets. This paper focuses on the artificial neural networks-based methods for visualization of big multidimensional datasets; namely,  on the approaches for the faster obtaining of visual results. The new strategy, which is identified by the decreased number of cycles of data reviews (passes of training data) up to the only one, when training neural networks, is proposed. To test this strategy, the results of experiments, using two unsupervised learning methods on benchmark data, are briefly presented.


Data visualization; big multidimensional dataset; neural networks-based method; decision support

Full Text:


DOI: 10.7250/csimq.2016-6.01


1. The examination of the effect of the criterion for neural network’s learning on the effectiveness of the qualitative analysis of multidimensional data
Dariusz Jamróz
Knowledge and Information Systems  vol: 62  issue: 8  first page: 3263  year: 2020  
doi: 10.1007/s10115-020-01441-8


  • There are currently no refbacks.

Copyright (c) 2016 Complex Systems Informatics and Modeling Quarterly